Overview

Dataset statistics

Number of variables16
Number of observations672
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.1 KiB
Average record size in memory128.2 B

Variable types

Numeric13
Categorical3

Alerts

시간당청색광량 has constant value "0"Constant
일간누적청색광량 has constant value "0"Constant
DAT is highly overall correlated with 내부온도관측치 and 3 other fieldsHigh correlation
내부온도관측치 is highly overall correlated with DAT and 6 other fieldsHigh correlation
내부습도관측치 is highly overall correlated with DAT and 7 other fieldsHigh correlation
시간당분무량 is highly overall correlated with obs_time and 7 other fieldsHigh correlation
일간누적분무량 is highly overall correlated with obs_time and 9 other fieldsHigh correlation
시간당백색광량 is highly overall correlated with obs_time and 9 other fieldsHigh correlation
일간누적백색광량 is highly overall correlated with obs_time and 7 other fieldsHigh correlation
시간당적색광량 is highly overall correlated with obs_time and 9 other fieldsHigh correlation
일간누적적색광량 is highly overall correlated with obs_time and 7 other fieldsHigh correlation
시간당총광량 is highly overall correlated with obs_time and 9 other fieldsHigh correlation
일간누적총광량 is highly overall correlated with obs_time and 7 other fieldsHigh correlation
obs_time is highly overall correlated with 내부습도관측치 and 8 other fieldsHigh correlation
co2관측치 is highly overall correlated with DAT and 1 other fieldsHigh correlation
ec관측치 is highly overall correlated with DAT and 3 other fieldsHigh correlation
obs_time is uniformly distributedUniform
DAT has 24 (3.6%) zerosZeros
ec관측치 has 25 (3.7%) zerosZeros
시간당분무량 has 147 (21.9%) zerosZeros
일간누적분무량 has 22 (3.3%) zerosZeros
시간당백색광량 has 252 (37.5%) zerosZeros
일간누적백색광량 has 168 (25.0%) zerosZeros
시간당적색광량 has 252 (37.5%) zerosZeros
일간누적적색광량 has 168 (25.0%) zerosZeros
시간당총광량 has 252 (37.5%) zerosZeros
일간누적총광량 has 168 (25.0%) zerosZeros

Reproduction

Analysis started2022-11-23 12:43:36.897630
Analysis finished2022-11-23 12:43:56.442183
Duration19.54 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

DAT
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.5
Minimum0
Maximum27
Zeros24
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:56.498584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16.75
median13.5
Q320.25
95-th percentile26
Maximum27
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.0837642
Coefficient of variation (CV)0.59879734
Kurtosis-1.2030827
Mean13.5
Median Absolute Deviation (MAD)7
Skewness0
Sum9072
Variance65.347243
MonotonicityIncreasing
2022-11-23T21:43:56.584653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 24
 
3.6%
1 24
 
3.6%
26 24
 
3.6%
25 24
 
3.6%
24 24
 
3.6%
23 24
 
3.6%
22 24
 
3.6%
21 24
 
3.6%
20 24
 
3.6%
19 24
 
3.6%
Other values (18) 432
64.3%
ValueCountFrequency (%)
0 24
3.6%
1 24
3.6%
2 24
3.6%
3 24
3.6%
4 24
3.6%
5 24
3.6%
6 24
3.6%
7 24
3.6%
8 24
3.6%
9 24
3.6%
ValueCountFrequency (%)
27 24
3.6%
26 24
3.6%
25 24
3.6%
24 24
3.6%
23 24
3.6%
22 24
3.6%
21 24
3.6%
20 24
3.6%
19 24
3.6%
18 24
3.6%

obs_time
Categorical

HIGH CORRELATION
UNIFORM

Distinct24
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
00:00
 
28
01:00
 
28
22:00
 
28
21:00
 
28
20:00
 
28
Other values (19)
532 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters3360
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row00:00
2nd row01:00
3rd row02:00
4th row03:00
5th row04:00

Common Values

ValueCountFrequency (%)
00:00 28
 
4.2%
01:00 28
 
4.2%
22:00 28
 
4.2%
21:00 28
 
4.2%
20:00 28
 
4.2%
19:00 28
 
4.2%
18:00 28
 
4.2%
17:00 28
 
4.2%
16:00 28
 
4.2%
15:00 28
 
4.2%
Other values (14) 392
58.3%

Length

2022-11-23T21:43:56.669688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00 28
 
4.2%
01:00 28
 
4.2%
02:00 28
 
4.2%
03:00 28
 
4.2%
04:00 28
 
4.2%
05:00 28
 
4.2%
06:00 28
 
4.2%
07:00 28
 
4.2%
08:00 28
 
4.2%
09:00 28
 
4.2%
Other values (14) 392
58.3%

Most occurring characters

ValueCountFrequency (%)
0 1708
50.8%
: 672
 
20.0%
1 364
 
10.8%
2 196
 
5.8%
3 84
 
2.5%
9 56
 
1.7%
8 56
 
1.7%
7 56
 
1.7%
6 56
 
1.7%
5 56
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2688
80.0%
Other Punctuation 672
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1708
63.5%
1 364
 
13.5%
2 196
 
7.3%
3 84
 
3.1%
9 56
 
2.1%
8 56
 
2.1%
7 56
 
2.1%
6 56
 
2.1%
5 56
 
2.1%
4 56
 
2.1%
Other Punctuation
ValueCountFrequency (%)
: 672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1708
50.8%
: 672
 
20.0%
1 364
 
10.8%
2 196
 
5.8%
3 84
 
2.5%
9 56
 
1.7%
8 56
 
1.7%
7 56
 
1.7%
6 56
 
1.7%
5 56
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1708
50.8%
: 672
 
20.0%
1 364
 
10.8%
2 196
 
5.8%
3 84
 
2.5%
9 56
 
1.7%
8 56
 
1.7%
7 56
 
1.7%
6 56
 
1.7%
5 56
 
1.7%

내부온도관측치
Real number (ℝ)

Distinct590
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.621507
Minimum25.081667
Maximum30.918333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:56.986266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum25.081667
5-th percentile25.200917
Q125.415833
median26.088334
Q327.695
95-th percentile29.168583
Maximum30.918333
Range5.8366664
Interquartile range (IQR)2.279167

Descriptive statistics

Standard deviation1.4165925
Coefficient of variation (CV)0.053212335
Kurtosis-0.41631349
Mean26.621507
Median Absolute Deviation (MAD)0.77250032
Skewness0.87246989
Sum17889.653
Variance2.0067344
MonotonicityNot monotonic
2022-11-23T21:43:57.084364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.58333365 3
 
0.4%
25.61500044 3
 
0.4%
25.93666643 3
 
0.4%
26.10000038 3
 
0.4%
25.46166658 2
 
0.3%
27.46999989 2
 
0.3%
25.33166663 2
 
0.3%
25.40166661 2
 
0.3%
25.46271185 2
 
0.3%
25.47000001 2
 
0.3%
Other values (580) 648
96.4%
ValueCountFrequency (%)
25.0816666 1
0.1%
25.09833333 1
0.1%
25.1016667 1
0.1%
25.12000008 1
0.1%
25.12241377 1
0.1%
25.12833325 1
0.1%
25.13000002 1
0.1%
25.13333321 1
0.1%
25.13666652 1
0.1%
25.14500052 1
0.1%
ValueCountFrequency (%)
30.91833302 1
0.1%
30.87999954 1
0.1%
30.85999947 1
0.1%
30.69000018 1
0.1%
30.46999989 1
0.1%
30.29999943 1
0.1%
30.28166641 1
0.1%
30.21999985 1
0.1%
30.08333365 1
0.1%
30.07500029 1
0.1%

내부습도관측치
Real number (ℝ)

Distinct608
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.17399
Minimum74.201665
Maximum85.366666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:57.191254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum74.201665
5-th percentile74.807583
Q176.733333
median81.455
Q382.757084
95-th percentile83.977833
Maximum85.366666
Range11.165002
Interquartile range (IQR)6.0237508

Descriptive statistics

Standard deviation3.1651345
Coefficient of variation (CV)0.039478321
Kurtosis-1.2165913
Mean80.17399
Median Absolute Deviation (MAD)1.8066668
Skewness-0.53617118
Sum53876.922
Variance10.018077
MonotonicityNot monotonic
2022-11-23T21:43:57.295132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81.67499987 4
 
0.6%
82.06166687 3
 
0.4%
81.30000051 2
 
0.3%
77.56166687 2
 
0.3%
75.9799998 2
 
0.3%
76.10666733 2
 
0.3%
82.44333318 2
 
0.3%
81.18666674 2
 
0.3%
80.90333354 2
 
0.3%
80.74166654 2
 
0.3%
Other values (598) 649
96.6%
ValueCountFrequency (%)
74.20166461 1
0.1%
74.20666491 1
0.1%
74.21166725 1
0.1%
74.21333377 1
0.1%
74.22333234 1
0.1%
74.23499959 1
0.1%
74.26833216 1
0.1%
74.28666751 1
0.1%
74.30666695 1
0.1%
74.33499997 1
0.1%
ValueCountFrequency (%)
85.36666616 1
0.1%
84.8549998 1
0.1%
84.563333 1
0.1%
84.55333303 1
0.1%
84.4766669 1
0.1%
84.46500028 1
0.1%
84.42833341 1
0.1%
84.3766669 1
0.1%
84.36333377 1
0.1%
84.29666723 1
0.1%

co2관측치
Real number (ℝ)

Distinct595
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean583.11902
Minimum464.43333
Maximum1264.3667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:57.401486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum464.43333
5-th percentile490.8325
Q1530.69608
median556.23333
Q3588.17083
95-th percentile815.56833
Maximum1264.3667
Range799.93333
Interquartile range (IQR)57.474755

Descriptive statistics

Standard deviation108.55757
Coefficient of variation (CV)0.18616709
Kurtosis11.9311
Mean583.11902
Median Absolute Deviation (MAD)28.191667
Skewness3.1230028
Sum391855.98
Variance11784.747
MonotonicityNot monotonic
2022-11-23T21:43:57.508614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
524.9 4
 
0.6%
517.6666667 3
 
0.4%
558.7666667 2
 
0.3%
544.1333333 2
 
0.3%
559.7 2
 
0.3%
564.4333333 2
 
0.3%
565.3166667 2
 
0.3%
565.8833333 2
 
0.3%
568.4666667 2
 
0.3%
564.0833333 2
 
0.3%
Other values (585) 649
96.6%
ValueCountFrequency (%)
464.4333333 1
0.1%
465.3166667 1
0.1%
468.8166667 1
0.1%
472.1666667 1
0.1%
473.6333333 1
0.1%
474.2166667 1
0.1%
474.5166667 1
0.1%
475.5833333 1
0.1%
475.9666667 1
0.1%
476.6666667 1
0.1%
ValueCountFrequency (%)
1264.366667 1
0.1%
1225.066667 1
0.1%
1220.583333 1
0.1%
1217.5 1
0.1%
1173.266667 1
0.1%
1077.85 1
0.1%
1068.566667 1
0.1%
1043.066667 1
0.1%
1015.65 1
0.1%
1013.566667 1
0.1%

ec관측치
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct589
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3582882
Minimum0
Maximum1.6863771
Zeros25
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:57.616121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.38034774
Q11.3987941
median1.4293951
Q31.4427631
95-th percentile1.598219
Maximum1.6863771
Range1.6863771
Interquartile range (IQR)0.043969004

Descriptive statistics

Standard deviation0.32515905
Coefficient of variation (CV)0.23938884
Kurtosis11.727682
Mean1.3582882
Median Absolute Deviation (MAD)0.022127035
Skewness-3.5512698
Sum912.7697
Variance0.10572841
MonotonicityNot monotonic
2022-11-23T21:43:57.711310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 25
 
3.7%
1.396299801 3
 
0.4%
1.4097742 2
 
0.3%
1.393881486 2
 
0.3%
1.41359761 2
 
0.3%
1.413420808 2
 
0.3%
1.413755387 2
 
0.3%
1.407417979 2
 
0.3%
1.40673272 2
 
0.3%
1.395641418 2
 
0.3%
Other values (579) 628
93.5%
ValueCountFrequency (%)
0 25
3.7%
0.000810324 1
 
0.1%
0.012939885 1
 
0.1%
0.01885271 1
 
0.1%
0.039426017 1
 
0.1%
0.088223923 1
 
0.1%
0.151125212 1
 
0.1%
0.214836823 1
 
0.1%
0.283790213 1
 
0.1%
0.34174094 1
 
0.1%
ValueCountFrequency (%)
1.68637706 1
0.1%
1.685202825 1
0.1%
1.684601037 1
0.1%
1.683984536 1
0.1%
1.68320662 1
0.1%
1.683191933 1
0.1%
1.682487402 1
0.1%
1.68223786 1
0.1%
1.678039996 1
0.1%
1.677085904 1
0.1%

시간당분무량
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct32
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean166.63129
Minimum0
Maximum252
Zeros147
Zeros (%)21.9%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:57.815343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1119.57
median245.57
Q3252
95-th percentile252
Maximum252
Range252
Interquartile range (IQR)132.43

Descriptive statistics

Standard deviation101.00183
Coefficient of variation (CV)0.60613963
Kurtosis-1.1227612
Mean166.63129
Median Absolute Deviation (MAD)6.43
Skewness-0.70516924
Sum111976.23
Variance10201.37
MonotonicityNot monotonic
2022-11-23T21:43:57.902024image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
252 187
27.8%
0 147
21.9%
126 108
16.1%
245.57 51
 
7.6%
245.57 50
 
7.4%
245.57 27
 
4.0%
245.57 15
 
2.2%
119.57 14
 
2.1%
119.57 12
 
1.8%
252 6
 
0.9%
Other values (22) 55
 
8.2%
ValueCountFrequency (%)
0 147
21.9%
74.56 1
 
0.1%
119.57 5
 
0.7%
119.57 1
 
0.1%
119.57 14
 
2.1%
119.57 12
 
1.8%
119.57 1
 
0.1%
126 108
16.1%
126 2
 
0.3%
155.55 1
 
0.1%
ValueCountFrequency (%)
252 2
 
0.3%
252 6
 
0.9%
252 5
 
0.7%
252 187
27.8%
252 4
 
0.6%
252 3
 
0.4%
245.57 50
 
7.4%
245.57 15
 
2.2%
245.57 51
 
7.6%
245.57 27
 
4.0%

일간누적분무량
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct241
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1967.4066
Minimum0
Maximum4125.85
Zeros22
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:58.003124image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile119.57
Q1462.855
median1877.14
Q33376.28
95-th percentile3974.13
Maximum4125.85
Range4125.85
Interquartile range (IQR)2913.425

Descriptive statistics

Standard deviation1416.5961
Coefficient of variation (CV)0.72003221
Kurtosis-1.4988382
Mean1967.4066
Median Absolute Deviation (MAD)1499.14
Skewness0.059602152
Sum1322097.2
Variance2006744.5
MonotonicityNot monotonic
2022-11-23T21:43:58.098529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126 44
 
6.5%
252 38
 
5.7%
0 22
 
3.3%
378 16
 
2.4%
245.57 16
 
2.4%
119.57 14
 
2.1%
371.57 12
 
1.8%
623.57 10
 
1.5%
869.14 8
 
1.2%
3696.41 7
 
1.0%
Other values (231) 485
72.2%
ValueCountFrequency (%)
0 22
3.3%
119.57 14
 
2.1%
126 44
6.5%
239.14 2
 
0.3%
245.57 16
 
2.4%
252 38
5.7%
326.56 2
 
0.3%
365.14 2
 
0.3%
371.57 12
 
1.8%
378 16
 
2.4%
ValueCountFrequency (%)
4125.85 2
0.3%
4119.42 1
 
0.1%
4106.56 2
0.3%
4093.7 1
 
0.1%
4074.41 2
0.3%
4048.69 2
0.3%
4035.83 2
0.3%
4029.4 2
0.3%
4012.71 2
0.3%
3999.85 3
0.4%

시간당백색광량
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10648.69
Minimum0
Maximum18564.6
Zeros252
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:58.193120image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18255.19
Q318255.19
95-th percentile18255.19
Maximum18564.6
Range18564.6
Interquartile range (IQR)18255.19

Descriptive statistics

Standard deviation8939.2449
Coefficient of variation (CV)0.83946894
Kurtosis-1.8882376
Mean10648.69
Median Absolute Deviation (MAD)3.092282 × 10-11
Skewness-0.33893663
Sum7155920
Variance79910099
MonotonicityNot monotonic
2022-11-23T21:43:58.287158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
18255.19 286
42.6%
0 252
37.5%
18255.19 22
 
3.3%
18255.19 18
 
2.7%
17945.78 16
 
2.4%
18255.19 10
 
1.5%
18255.19 9
 
1.3%
928.23 8
 
1.2%
17636.37 5
 
0.7%
618.82 4
 
0.6%
Other values (31) 42
 
6.2%
ValueCountFrequency (%)
0 252
37.5%
309.41 1
 
0.1%
485.7737 1
 
0.1%
513.6206 2
 
0.3%
550.7498 1
 
0.1%
618.82 4
 
0.6%
724.0194 1
 
0.1%
894.1949 1
 
0.1%
928.23 4
 
0.6%
928.23 8
 
1.2%
ValueCountFrequency (%)
18564.6 3
 
0.4%
18564.6 1
 
0.1%
18255.19 9
 
1.3%
18255.19 1
 
0.1%
18255.19 286
42.6%
18255.19 10
 
1.5%
18255.19 22
 
3.3%
18255.19 18
 
2.7%
17945.78 1
 
0.1%
17945.78 16
 
2.4%

일간누적백색광량
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct258
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122001.38
Minimum0
Maximum256324.53
Zeros168
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:58.389753image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112763.162
median117885.21
Q3223027.37
95-th percentile255708.8
Maximum256324.53
Range256324.53
Interquartile range (IQR)210264.21

Descriptive statistics

Standard deviation99907.825
Coefficient of variation (CV)0.81890736
Kurtosis-1.5655672
Mean122001.38
Median Absolute Deviation (MAD)109376.43
Skewness0.079195764
Sum81984925
Variance9.9815735 × 109
MonotonicityNot monotonic
2022-11-23T21:43:58.493931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 168
 
25.0%
254953.84 13
 
1.9%
255572.66 12
 
1.8%
255263.25 10
 
1.5%
256324.5263 8
 
1.2%
255829.4703 8
 
1.2%
255467.4606 8
 
1.2%
127167.51 7
 
1.0%
163677.89 7
 
1.0%
181933.08 7
 
1.0%
Other values (248) 424
63.1%
ValueCountFrequency (%)
0 168
25.0%
17017.55 1
 
0.1%
17097.9966 1
 
0.1%
17240.3252 1
 
0.1%
17314.5836 1
 
0.1%
17326.96 4
 
0.6%
17376.4656 1
 
0.1%
17413.5948 1
 
0.1%
17422.8771 1
 
0.1%
17463.1004 1
 
0.1%
ValueCountFrequency (%)
256324.5263 8
1.2%
256163.6331 4
 
0.6%
255829.4703 8
1.2%
255795.4352 4
 
0.6%
255767.5883 4
 
0.6%
255724.2709 4
 
0.6%
255708.8004 4
 
0.6%
255677.8594 4
 
0.6%
255597.4128 4
 
0.6%
255572.66 12
1.8%

시간당적색광량
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean569.53685
Minimum0
Maximum992.88
Zeros252
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:58.591301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median976.332
Q3976.332
95-th percentile976.332
Maximum992.88
Range992.88
Interquartile range (IQR)976.332

Descriptive statistics

Standard deviation478.06207
Coefficient of variation (CV)0.83938742
Kurtosis-1.8881904
Mean569.53685
Median Absolute Deviation (MAD)2.0463631 × 10-12
Skewness-0.3389416
Sum382728.76
Variance228543.34
MonotonicityNot monotonic
2022-11-23T21:43:58.689411image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 252
37.5%
976.332 196
29.2%
976.332 77
 
11.5%
976.332 51
 
7.6%
976.332 21
 
3.1%
959.784 9
 
1.3%
959.784 9
 
1.3%
49.644 7
 
1.0%
33.096 6
 
0.9%
926.688 5
 
0.7%
Other values (27) 39
 
5.8%
ValueCountFrequency (%)
0 252
37.5%
21.5124 1
 
0.1%
26.4768 2
 
0.3%
33.096 6
 
0.9%
43.0248 2
 
0.3%
46.3344 2
 
0.3%
49.644 3
 
0.4%
49.644 7
 
1.0%
56.2632 2
 
0.3%
66.192 1
 
0.1%
ValueCountFrequency (%)
992.88 2
 
0.3%
992.88 1
 
0.1%
992.88 1
 
0.1%
976.332 1
 
0.1%
976.332 196
29.2%
976.332 77
 
11.5%
976.332 51
 
7.6%
976.332 21
 
3.1%
959.784 9
 
1.3%
959.784 9
 
1.3%

일간누적적색광량
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct251
Distinct (%)37.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6524.9306
Minimum0
Maximum13708.363
Zeros168
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:58.783791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1682.605
median6303.9606
Q311927.798
95-th percentile13675.267
Maximum13708.363
Range13708.363
Interquartile range (IQR)11245.193

Descriptive statistics

Standard deviation5343.406
Coefficient of variation (CV)0.81892151
Kurtosis-1.5655411
Mean6524.9306
Median Absolute Deviation (MAD)5849.718
Skewness0.079244731
Sum4384753.3
Variance28551988
MonotonicityNot monotonic
2022-11-23T21:43:58.880244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 168
25.0%
13662.0288 16
 
2.4%
13652.1 15
 
2.2%
13635.552 12
 
1.8%
13657.0644 8
 
1.2%
13708.3632 8
 
1.2%
13681.8864 8
 
1.2%
13668.648 8
 
1.2%
7761.012 6
 
0.9%
959.784 5
 
0.7%
Other values (241) 418
62.2%
ValueCountFrequency (%)
0 168
25.0%
910.14 1
 
0.1%
913.4496 1
 
0.1%
920.0688 1
 
0.1%
925.0332 1
 
0.1%
926.688 5
 
0.7%
928.3428 1
 
0.1%
929.9976 1
 
0.1%
931.6524 1
 
0.1%
934.962 1
 
0.1%
ValueCountFrequency (%)
13708.3632 8
1.2%
13698.4344 4
0.6%
13696.7796 4
0.6%
13683.5412 4
0.6%
13681.8864 8
1.2%
13678.5768 4
0.6%
13675.2672 4
0.6%
13671.9576 4
0.6%
13670.3028 4
0.6%
13668.648 8
1.2%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
672 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters672
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 672
100.0%

Length

2022-11-23T21:43:58.982945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T21:43:59.079026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 672
100.0%

Most occurring characters

ValueCountFrequency (%)
0 672
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 672
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 672
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 672
100.0%
Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size5.4 KiB
0
672 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters672
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 672
100.0%

Length

2022-11-23T21:43:59.145010image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-23T21:43:59.226532image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0 672
100.0%

Most occurring characters

ValueCountFrequency (%)
0 672
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 672
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 672
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 672
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 672
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 672
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 672
100.0%

시간당총광량
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct49
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11218.227
Minimum0
Maximum19557.48
Zeros252
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:59.307404image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median19231.522
Q319231.522
95-th percentile19231.522
Maximum19557.48
Range19557.48
Interquartile range (IQR)19231.522

Descriptive statistics

Standard deviation9417.3029
Coefficient of variation (CV)0.83946444
Kurtosis-1.8882384
Mean11218.227
Median Absolute Deviation (MAD)5.4569682 × 10-11
Skewness-0.33893616
Sum7538648.8
Variance88685594
MonotonicityNot monotonic
2022-11-23T21:43:59.411331image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 252
37.5%
19231.522 196
29.2%
19231.522 40
 
6.0%
19231.522 33
 
4.9%
19231.522 31
 
4.6%
19231.522 27
 
4.0%
19231.522 14
 
2.1%
977.874 9
 
1.3%
18905.564 8
 
1.2%
19231.522 5
 
0.7%
Other values (39) 57
 
8.5%
ValueCountFrequency (%)
0 252
37.5%
342.506 1
 
0.1%
507.2861 1
 
0.1%
540.0974 2
 
0.3%
593.7746 1
 
0.1%
651.916 2
 
0.3%
651.916 2
 
0.3%
770.3538 1
 
0.1%
940.5293 1
 
0.1%
961.326 1
 
0.1%
ValueCountFrequency (%)
19557.48 2
 
0.3%
19557.48 2
 
0.3%
19231.522 40
 
6.0%
19231.522 31
 
4.6%
19231.522 33
 
4.9%
19231.522 196
29.2%
19231.522 5
 
0.7%
19231.522 14
 
2.1%
19231.522 27
 
4.0%
18905.564 5
 
0.7%

일간누적총광량
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct287
Distinct (%)42.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128526.31
Minimum0
Maximum270032.89
Zeros168
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size5.4 KiB
2022-11-23T21:43:59.520384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113445.767
median124190
Q3234955.17
95-th percentile269380.76
Maximum270032.89
Range270032.89
Interquartile range (IQR)221509.4

Descriptive statistics

Standard deviation105251.23
Coefficient of variation (CV)0.81890805
Kurtosis-1.5655662
Mean128526.31
Median Absolute Deviation (MAD)115226.15
Skewness0.0791981
Sum86369678
Variance1.1077821 × 1010
MonotonicityNot monotonic
2022-11-23T21:43:59.622940image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 168
 
25.0%
268915.35 10
 
1.5%
269511.3567 8
 
1.2%
268589.392 8
 
1.2%
269129.4894 8
 
1.2%
270032.8895 8
 
1.2%
269241.308 8
 
1.2%
210568.868 5
 
0.7%
268605.94 5
 
0.7%
229800.39 5
 
0.7%
Other values (277) 439
65.3%
ValueCountFrequency (%)
0 168
25.0%
17927.69 1
 
0.1%
18011.4462 1
 
0.1%
18160.394 1
 
0.1%
18239.6168 1
 
0.1%
18253.648 4
 
0.6%
18321.3564 1
 
0.1%
18341.9376 1
 
0.1%
18362.8035 1
 
0.1%
18393.098 1
 
0.1%
ValueCountFrequency (%)
270032.8895 8
1.2%
269860.4127 4
0.6%
269511.3567 8
1.2%
269457.464 4
0.6%
269431.2719 4
0.6%
269399.5381 4
0.6%
269380.758 4
0.6%
269376.2938 4
0.6%
269264.406 4
0.6%
269243.8248 4
0.6%

Interactions

2022-11-23T21:43:54.921087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:39.974162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.186671image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.499872image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.690732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.989191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.459646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.634954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.800079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.013630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.353846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.496663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.691450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.007541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.079782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.274241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.587402image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.785050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:45.083033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.547526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.715610image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.889167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.099301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.435659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.585203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.780213image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.094135image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.192786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.361379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.677560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.882351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:45.181471image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.637079image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.810909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.992756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.190743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.522417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.677564image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.879562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.182998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.286904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.446336image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.764071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.984841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:45.273477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.725131image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.901245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.082572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.274505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.610743image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.769570image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.987757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.278618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.382354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.538823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.868000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.097972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:45.377332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.819792image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.998558image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.187988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.371138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.705246image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.864752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:54.082048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.369926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.469293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.772396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.959553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.202004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:45.483711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.915539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.090101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.279355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.463295image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.799706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.963880image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:54.178505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.453794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.559871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.866814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.049963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.304272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:45.577304image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.013279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.179011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.367901image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.546684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.882644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.048289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:54.270877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.534034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.640318image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.954656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.135396image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.390830image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:45.691730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.099770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.261449image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.457171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.626881image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.963587image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.138719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:54.357667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.631724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.731891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.051653image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.231149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.494025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:45.947257image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.203648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.356489image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.551973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.906461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.054662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.237225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:54.453045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.717884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.821944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.137622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.319973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.589868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.042590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.285002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.442816image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.641815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:50.994428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.137572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.326186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:54.543136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.803198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:40.908988image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.225620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.408552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.686050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.144495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.365441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.528333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.732660image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.076608image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.224080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.413931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:54.636310image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.895820image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.004603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.316391image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.504949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.790287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.248237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.456755image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.615998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.828552image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.170918image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.319824image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.510917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:54.732629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:55.992252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:41.101523image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:42.412613image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:43.603414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:44.893623image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:46.365972image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:47.551377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:48.717390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:49.924444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:51.271460image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:52.409161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:53.605110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-11-23T21:43:54.828775image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-11-23T21:43:59.730877image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-23T21:43:59.892158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T21:44:00.060659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T21:44:00.243014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T21:44:00.423390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T21:43:56.137463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T21:43:56.351201image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DATobs_time내부온도관측치내부습도관측치co2관측치ec관측치시간당분무량일간누적분무량시간당백색광량일간누적백색광량시간당적색광량일간누적적색광량시간당청색광량일간누적청색광량시간당총광량일간누적총광량
0000:0025.30000081.835000536.0166671.4074390.000.000.000.000.0000.000000.0000.000
1001:0025.68035781.264286528.6964291.409003126.00126.000.000.000.0000.000000.0000.000
2002:0025.27333381.471666532.8333331.4069130.00126.000.000.000.0000.000000.0000.000
3003:0025.35500081.398334545.5666671.406689126.00252.000.000.000.0000.000000.0000.000
4004:0025.39166781.483333558.5833331.4110700.00252.000.000.000.0000.000000.0000.000
5005:0025.37500081.506666571.4833331.409658119.57371.570.000.000.0000.000000.0000.000
6006:0026.43666776.570000575.0833331.404834252.00623.5717636.3717636.37959.784959.7840018596.15418596.154
7007:0026.96000075.783334574.5500001.400731239.14862.7118255.1935891.56976.3321936.1160019231.52237827.676
8008:0027.12833475.568333570.5000001.398819245.571108.2818255.1954146.75976.3322912.4480019231.52257059.198
9009:0027.36666675.541666568.1000001.396300252.001360.2818255.1972401.94976.3323888.7800019231.52276290.720
DATobs_time내부온도관측치내부습도관측치co2관측치ec관측치시간당분무량일간누적분무량시간당백색광량일간누적백색광량시간당적색광량일간누적적색광량시간당청색광량일간누적청색광량시간당총광량일간누적총광량
6622714:0030.28166683.190000495.5000000.0252.002391.4018255.19163368.48976.3328737.3440019231.522172105.824
6632715:0030.47000082.856667485.6333330.0252.002643.4018255.19181623.67976.3329713.6760019231.522191337.346
6642716:0030.69000083.160000477.5666670.0245.572888.9718255.19199878.86976.33210690.0080019231.522210568.868
6652717:0030.85999982.635000468.8166670.0245.573134.5418255.19218134.05976.33211666.3400019231.522229800.390
6662718:0030.91833382.628334465.3166670.0252.003386.5418255.19236389.24976.33212642.6720019231.522249031.912
6672719:0030.88000082.255000464.4333330.0252.003638.5418255.19254644.43976.33213619.0040019231.522268263.434
6682720:0029.06833382.506667534.6333330.0126.003764.54618.82255263.2533.09613652.10000651.916268915.350
6692721:0028.24666782.835000563.4333330.00.003764.540.00255263.250.00013652.100000.000268915.350
6702722:0028.00500082.850000577.1500000.0126.003890.540.00255263.250.00013652.100000.000268915.350
6712723:0027.86833382.453333588.6166670.00.003890.540.00255263.250.00013652.100000.000268915.350